



'''simple docstring'''

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


SCREAMING_SNAKE_CASE__     :						int	=  [
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
SCREAMING_SNAKE_CASE__     :						Dict	=  [
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

SCREAMING_SNAKE_CASE__     :						Dict	=  (
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  (
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE__     :						Optional[int]	=  [
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    Union[str, Any]			)       ->	Dict:
		for tf_name, hf_name in patterns:
				snake_case__	     =k.replace(UpperCamelCase_      ,					UpperCamelCase_			)
		return k






def 				a  (     UpperCamelCase_    :    dict      ,					UpperCamelCase_    :    dict			)       ->	BigBirdPegasusForConditionalGeneration:
		snake_case__	     =BigBirdPegasusConfig(**UpperCamelCase_			)
		snake_case__	     =BigBirdPegasusForConditionalGeneration(UpperCamelCase_			)
		snake_case__	     =torch_model.state_dict()
		snake_case__	     ={}

		# separating decoder weights
		snake_case__	     ={k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'			)}
		snake_case__	     ={k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'			)}

		for k, v in tqdm(decoder_weights.items()      ,					'tf -> hf conversion'			):
				snake_case__	     =[k.endswith(UpperCamelCase_			) for ending in KEYS_TO_IGNORE]
				if any(UpperCamelCase_			):
						continue
				snake_case__	     =DECODER_PATTERNS
				snake_case__	     =rename_state_dict_key(UpperCamelCase_      ,					UpperCamelCase_			)
				if new_k not in state_dict:
						raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""			)
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']			):
						snake_case__	     =v.T
				snake_case__	     =torch.from_numpy(UpperCamelCase_			)
				assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

		for k, v in tqdm(remaining_weights.items()      ,					'tf -> hf conversion'			):
				snake_case__	     =[k.endswith(UpperCamelCase_			) for ending in KEYS_TO_IGNORE]
				if any(UpperCamelCase_			):
						continue
				snake_case__	     =REMAINING_PATTERNS
				snake_case__	     =rename_state_dict_key(UpperCamelCase_      ,					UpperCamelCase_			)
				if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
						raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""			)
				if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']			):
						snake_case__	     =v.T
				snake_case__	     =torch.from_numpy(UpperCamelCase_			)
				if k != "pegasus/embeddings/position_embeddings":
						assert v.shape == state_dict[new_k].shape, f"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

		snake_case__	     =mapping['model.embed_positions.weight']
		snake_case__	     =mapping.pop('model.embed_positions.weight'			)
		snake_case__     ,	snake_case__	     =torch_model.load_state_dict(UpperCamelCase_      ,					strict=UpperCamelCase_			)
		snake_case__	     =[
		    k
		    for k in missing
		    if k
		    not in [
		        'final_logits_bias',
		        'model.encoder.embed_tokens.weight',
		        'model.decoder.embed_tokens.weight',
		        'lm_head.weight',
		    ]
		]
		assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
		assert extra == [], f"""no matches found for the following tf keys {extra}"""
		return torch_model






def 				a  (     UpperCamelCase_    :    Any			)       ->	Dict:
		snake_case__	     =tf.train.list_variables(UpperCamelCase_			)
		snake_case__	     ={}
		snake_case__	     =['global_step']
		for name, shape in tqdm(UpperCamelCase_      ,					desc='converting tf checkpoint to dict'			):
				snake_case__	     =any(pat in name for pat in ignore_name			)
				if skip_key:
						continue
				snake_case__	     =tf.train.load_variable(UpperCamelCase_      ,					UpperCamelCase_			)
				snake_case__	     =array
		return tf_weights






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    str      ,					UpperCamelCase_    :    dict			)       ->	Tuple:
		snake_case__	     =get_tf_weights_as_numpy(UpperCamelCase_			)
		snake_case__	     =convert_bigbird_pegasus(UpperCamelCase_      ,					UpperCamelCase_			)
		torch_model.save_pretrained(UpperCamelCase_			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Tuple	=  argparse.ArgumentParser()
			parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
			parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
			SCREAMING_SNAKE_CASE__     :						Tuple	=  parser.parse_args()
			SCREAMING_SNAKE_CASE__     :						Tuple	=  {}
			convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)









'''simple docstring'''

import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
SCREAMING_SNAKE_CASE__     :						Any	=  get_tests_dir('''fixtures/vocab.json''')
SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  get_tests_dir('''fixtures''')

class 							a__( unittest.TestCase     ):
			a_	: Union[str, Any]									=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =0






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)
					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaConfig()
							snake_case__	     =AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)

							# save in new folder
							model_config.save_pretrained(_UpperCAmelCase						)
							processor.save_pretrained(_UpperCAmelCase						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Tuple:
					with tempfile.TemporaryDirectory() as tmpdirname:
							# copy relevant files
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)						)
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   'vocab.json'						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaFeatureExtractor()
							snake_case__	     =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

							snake_case__	     =WavaVecaProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							# save in new folder
							processor.save_pretrained(_UpperCAmelCase						)

							# drop `processor_class` in tokenizer
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'r'						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
									config_dict.pop('processor_class'						)

							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write(json.dumps(_UpperCAmelCase						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[int]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaFeatureExtractor()
							snake_case__	     =AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

							snake_case__	     =WavaVecaProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							# save in new folder
							processor.save_pretrained(_UpperCAmelCase						)

							# drop `processor_class` in feature extractor
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'r'						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
									config_dict.pop('processor_class'						)

							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write(json.dumps(_UpperCAmelCase						)						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							List[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
							snake_case__	     =WavaVecaConfig(processor_class='Wav2Vec2Processor'						)
							model_config.save_pretrained(_UpperCAmelCase						)
							# copy relevant files
							copyfile(_UpperCAmelCase					,   os.path.join(_UpperCAmelCase					,   'vocab.json'						)						)
							# create emtpy sample processor
							with open(os.path.join(_UpperCAmelCase					,   _UpperCAmelCase						)					,   'w'						) as f:
									f.write('{}'						)

							snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)

					self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							int:
					# If remote code is not set, we will time out when asking whether to load the model.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
					# If remote code is disabled, we can't load this config.
					with self.assertRaises(_UpperCAmelCase						):
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)

					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
					self.assertTrue(processor.special_attribute_present						)
					self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)

					snake_case__	     =processor.feature_extractor
					self.assertTrue(feature_extractor.special_attribute_present						)
					self.assertEqual(feature_extractor.__class__.__name__					,   'NewFeatureExtractor'						)

					snake_case__	     =processor.tokenizer
					self.assertTrue(tokenizer.special_attribute_present						)
					if is_tokenizers_available():
							self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizerFast'						)

							# Test we can also load the slow version
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase					,   use_fast=_UpperCAmelCase						)
							snake_case__	     =new_processor.tokenizer
							self.assertTrue(new_tokenizer.special_attribute_present						)
							self.assertEqual(new_tokenizer.__class__.__name__					,   'NewTokenizer'						)
					else:
							self.assertEqual(tokenizer.__class__.__name__					,   'NewTokenizer'						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoFeatureExtractor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							AutoTokenizer.register(_UpperCAmelCase					,   slow_tokenizer_class=_UpperCAmelCase						)
							AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# Trying to register something existing in the Transformers library will raise an error
							with self.assertRaises(_UpperCAmelCase						):
									AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)

							# Now that the config is registered, it can be used as any other config with the auto-API
							snake_case__	     =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									snake_case__	     =os.path.join(_UpperCAmelCase					,   'vocab.txt'						)
									with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
									snake_case__	     =CustomTokenizer(_UpperCAmelCase						)

							snake_case__	     =CustomProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(_UpperCAmelCase						)
									snake_case__	     =AutoProcessor.from_pretrained(_UpperCAmelCase						)
									self.assertIsInstance(_UpperCAmelCase					,   _UpperCAmelCase						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 						_lowercase  (     self						)							->							Union[str, Any]:

					class 							a__( snake_case__     ):
								a_	: Optional[int]									=      False





					class 							a__( snake_case__     ):
								a_	: List[Any]									=      False





					class 							a__( snake_case__     ):
								a_	: int									=      '''AutoFeatureExtractor'''
								a_	: int									=      '''AutoTokenizer'''
								a_	: List[str]									=      False

					try:
							AutoConfig.register('custom'					,   _UpperCAmelCase						)
							AutoFeatureExtractor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							AutoTokenizer.register(_UpperCAmelCase					,   slow_tokenizer_class=_UpperCAmelCase						)
							AutoProcessor.register(_UpperCAmelCase					,   _UpperCAmelCase						)
							# If remote code is not set, the default is to use local classes.
							snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertFalse(processor.special_attribute_present						)
							self.assertFalse(processor.feature_extractor.special_attribute_present						)
							self.assertFalse(processor.tokenizer.special_attribute_present						)

							# If remote code is disabled, we load the local ones.
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertFalse(processor.special_attribute_present						)
							self.assertFalse(processor.feature_extractor.special_attribute_present						)
							self.assertFalse(processor.tokenizer.special_attribute_present						)

							# If remote is enabled, we load from the Hub.
							snake_case__	     =AutoProcessor.from_pretrained(
							    'hf-internal-testing/test_dynamic_processor'					,   trust_remote_code=_UpperCAmelCase						)
							self.assertEqual(processor.__class__.__name__					,   'NewProcessor'						)
							self.assertTrue(processor.special_attribute_present						)
							self.assertTrue(processor.feature_extractor.special_attribute_present						)
							self.assertTrue(processor.tokenizer.special_attribute_present						)

					finally:
							if "custom" in CONFIG_MAPPING._extra_content:
									del CONFIG_MAPPING._extra_content["custom"]
							if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
									del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
							if CustomConfig in TOKENIZER_MAPPING._extra_content:
									del TOKENIZER_MAPPING._extra_content[CustomConfig]
							if CustomConfig in PROCESSOR_MAPPING._extra_content:
									del PROCESSOR_MAPPING._extra_content[CustomConfig]






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'						)
					self.assertEqual(processor.__class__.__name__					,   'BertTokenizerFast'						)






			def 						_lowercase  (     self						)							->							List[Any]:
					snake_case__	     =AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'						)
					self.assertEqual(processor.__class__.__name__					,   'ConvNextImageProcessor'						)





@is_staging_test
class 							a__( unittest.TestCase     ):
			a_	: Dict									=      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']






			@classmethod
			def 						_lowercase  (     cls						)							->							str:
					snake_case__	     =TOKEN
					HfFolder.save_token(_UpperCAmelCase						)






			@classmethod
			def 						_lowercase  (     cls						)							->							int:
					try:
							delete_repo(token=cls._token					,   repo_id='test-processor'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='valid_org/test-processor-org'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='test-dynamic-processor'						)
					except HTTPError:
							pass






			def 						_lowercase  (     self						)							->							int:
					snake_case__	     =WavaVecaProcessor.from_pretrained(_UpperCAmelCase						)
					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(_UpperCAmelCase					,   'test-processor'						)					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token						)

							snake_case__	     =WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""						)
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(_UpperCAmelCase					,   getattr(new_processor.feature_extractor					,   _UpperCAmelCase						)						)
							self.assertDictEqual(new_processor.tokenizer.get_vocab()					,   processor.tokenizer.get_vocab()						)






			def 						_lowercase  (     self						)							->							List[str]:
					snake_case__	     =WavaVecaProcessor.from_pretrained(_UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							processor.save_pretrained(
							    os.path.join(_UpperCAmelCase					,   'test-processor-org'						)					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token					,   organization='valid_org'					,   )

							snake_case__	     =WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'						)
							for k, v in processor.feature_extractor.__dict__.items():
									self.assertEqual(_UpperCAmelCase					,   getattr(new_processor.feature_extractor					,   _UpperCAmelCase						)						)
							self.assertDictEqual(new_processor.tokenizer.get_vocab()					,   processor.tokenizer.get_vocab()						)






			def 						_lowercase  (     self						)							->							Any:
					CustomFeatureExtractor.register_for_auto_class()
					CustomTokenizer.register_for_auto_class()
					CustomProcessor.register_for_auto_class()

					snake_case__	     =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							snake_case__	     =os.path.join(_UpperCAmelCase					,   'vocab.txt'						)
							with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
							snake_case__	     =CustomTokenizer(_UpperCAmelCase						)

					snake_case__	     =CustomProcessor(_UpperCAmelCase					,   _UpperCAmelCase						)

					with tempfile.TemporaryDirectory() as tmp_dir:
							create_repo(f"""{USER}/test-dynamic-processor"""					,   token=self._token						)
							snake_case__	     =Repository(_UpperCAmelCase					,   clone_from=f"""{USER}/test-dynamic-processor"""					,   token=self._token						)
							processor.save_pretrained(_UpperCAmelCase						)

							# This has added the proper auto_map field to the feature extractor config
							self.assertDictEqual(
							    processor.feature_extractor.auto_map					,   {
							        'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
							        'AutoProcessor': 'custom_processing.CustomProcessor',
							    }					,   )

							# This has added the proper auto_map field to the tokenizer config
							with open(os.path.join(_UpperCAmelCase					,   'tokenizer_config.json'						)						) as f:
									snake_case__	     =json.load(_UpperCAmelCase						)
							self.assertDictEqual(
							    tokenizer_config['auto_map']					,   {
							        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
							        'AutoProcessor': 'custom_processing.CustomProcessor',
							    }					,   )

							# The code has been copied from fixtures
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_feature_extraction.py'						)						)						)
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_tokenization.py'						)						)						)
							self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase					,   'custom_processing.py'						)						)						)

							repo.push_to_hub()

					snake_case__	     =AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""					,   trust_remote_code=_UpperCAmelCase						)
					# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
					self.assertEqual(new_processor.__class__.__name__					,   'CustomProcessor'						)




